5.1 Empirical results
Table
4 shows the estimation results for the influence of FDI on growth without control variables. Column 1 reports the effect of FDI on growth while accounting for the province fixed effect. The result shows that FDI has a positive and statistically significant impact on growth. Columns 2 and 3 report the magnitude of the relationship between FDI and growth by controlling for province and year fixed effects (Column 2) and province, year, and sector fixed effects (Column 3). The results did not change, where FDI has a statistically significant positive effect. However, the association's size increased also the R-squared is sharply improved (Column 3). This result indicates that control for the sector is crucial to single out the impact of FDI on growth using sectoral data.
Table 4
Baseline regression—the impact of FDI on economic growth
lfdi | 0.0925*** | 0.0925*** | 0.0926*** | 0.0926*** | 0.0899*** | 0.0929*** | 0.0906*** |
| (0.0115) | (0.0116) | (0.00656) | (0.00656) | (0.0117) | (0.00660) | (0.0116) |
Province FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Year FE | No | Yes | Yes | Yes | No | No | No |
Sector FE | No | No | Yes | Yes | No | No | No |
Island FE | No | No | No | Yes | No | No | No |
Year × Province FE | No | No | No | No | Yes | No | No |
Year × Sector FE | No | No | No | No | No | Yes | No |
Year × Island FE | No | No | No | No | No | No | Yes |
Observations | 2380 | 2380 | 2380 | 2380 | 2380 | 2380 | 2380 |
R-squared | 0.391 | 0.392 | 0.847 | 0.847 | 0.395 | 0.847 | 0.392 |
In Column 4, we introduce island fixed effect to capture the unobserved heterogeneity and does not change over time that could arise from an island since Indonesia is an archipelagos country. In this setting we divide into six archipelagos: Sumatra, Java, Kalimantan, Bali and Nusa Tenggara, Sulawesi, Maluku, and Papua. The result shows that FDI has to remain positively affect growth.
Columns 5 and 6 report the estimation results of the effect of FDI on growth by controlling for the province fixed effect and adding the interaction between province and year fixed effect (Column 5) and sector and year fixed effect (Column 6). The results describe that FDI has a positive and statistically significant impact, where the relationship is slightly increased (Column 6). Further, in Column 7, we control for province fixed effect and add the interaction between island and year fixed effect to explain the effect of FDI on growth. The result is still robust, where FDI positively impacts growth and relatively high magnitude.
Table
5 shows the estimation results of the growth impact of FDI while controlling for other determinants such as population, education, domestic investment, government expenditure, bank lending, and inflation. Column 1 presents the estimation results of the effect of FDI on growth by including control variables and controlling for the province fixed effect. The results show that FDI has a positive and statistically significant impact on growth, with a stable magnitude compared to the finding in Table
4. All control variables, such as population, education, domestic investment, government spending, bank lending, and inflation, statistically insignificant affect growth. However, the signs are mixed, with negative consequences for population, education, and inflation. Meanwhile, domestic investment, government spending, and bank lending are positive signs.
Table 5
Main regression—the impact of FDI on economic growth
lfdi | 0.0917*** | 0.0924*** | 0.0924*** | 0.0924*** | 0.0905*** | 0.0925*** | 0.0908*** |
| (0.0116) | (0.0117) | (0.00657) | (0.00657) | (0.0118) | (0.00662) | (0.0116) |
lpop | − 1.943 | − 2.600* | − 0.939 | − 0.939 | − 1.706 | − 0.756 | − 2.082 |
| (1.401) | (1.542) | (0.776) | (0.776) | (2.413) | (0.753) | (1.611) |
ys | − 0.0948 | − 0.0456 | − 0.0174 | − 0.0174 | − 0.0897 | − 0.0209 | − 0.110 |
| (0.114) | (0.137) | (0.0691) | (0.0691) | (0.130) | (0.0577) | (0.116) |
ldi | 0.255 | − 0.0266 | 0.136 | 0.136 | − 0.350 | 0.196 | − 0.127 |
| (0.462) | (0.515) | (0.260) | (0.260) | (0.927) | (0.256) | (0.581) |
lgov | 0.451 | 0.434 | 0.371 | 0.371 | − 0.421 | 0.334 | 0.196 |
| (0.711) | (0.740) | (0.372) | (0.372) | (1.070) | (0.360) | (0.732) |
lcre | 0.0848 | 0.262 | 0.253 | 0.253 | 0.0587 | 0.0227 | − 0.0102 |
| (0.298) | (0.444) | (0.223) | (0.223) | (0.397) | (0.160) | (0.329) |
inf | − 0.00813 | 0.00280 | − 0.00500 | − 0.00500 | − 0.00599 | − 0.00506 | − 0.00641 |
| (0.0148) | (0.0284) | (0.0143) | (0.0143) | (0.0160) | (0.00778) | (0.0156) |
Province FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Year FE | No | Yes | Yes | Yes | No | No | No |
Sector FE | No | No | Yes | Yes | No | No | No |
Island FE | No | No | No | Yes | No | No | No |
Year × Province FE | No | No | No | No | Yes | No | No |
Year × Sector FE | No | No | No | No | No | Yes | No |
Year × Island FE | No | No | No | No | No | No | Yes |
Observations | 2380 | 2380 | 2380 | 2380 | 2380 | 2380 | 2380 |
R-squared | 0.392 | 0.393 | 0.847 | 0.847 | 0.395 | 0.848 | 0.393 |
Columns 2 and 3 show the estimation results of the growth effect of FDI by adding control variables and controlling for province and year fixed effects (Column 2) and controlling for province, year, and sector fixed effects (Column 3). The results show that FDI has a statistically significant positive effect on growth. Meanwhile, the effect of control variables remains statistically insignificant except for population, which becomes statistically significant and has a negative sign.
Column 4 describes the estimation results of the effect of FDI on the growth by controlling for the control variables and province, year, sector, and island fixed effect. The result shows that FDI has to remain positively affect growth and statistically significant with the size of magnitude no significant difference from previous results in Columns 2 and 3.
Columns 5 and 6 report the estimation results of the FDI's influence on growth by adding control variables and controlling for province fixed effect, the interaction between province and year fixed effect (Column 5) and the interaction between sector and year fixed effect (Column 6). The results show that FDI still has a statistically significant positive effect on growth, the magnitude of the relationship increases (Column 6). Meanwhile, the effect of all control variables is still statistically insignificant.
Column 7 shows the estimation result from FDI's effect on growth by adding control variables and controlling for province fixed effect and the interaction between year and island fixed effect. The result shows that the impact of FDI on growth is positive and statistically significant, whereas the control variables also indicate statistically no significance.
According to earlier work, such as Adams (
2009) and Chaudhury et al. (
2020), FDI in affecting economic growth takes time. As a result, past research proposes using lagged FDI. In this work, we included a lagged FDI variable (L.lfdi) in the regression model estimate. This lag FDI variable is defined as the FDI value in period
t-1 .
Table
6 shows the influence of lagged FDI on economic growth that we added to the model. The estimation findings in Table
6 (for all columns) reveal that lagged FDI and FDI in the current period have a statistically significant and beneficial impact on economic growth. The magnitude of FDI for the current period and lagged FDI does not reveal a significant difference, with the coefficient value in the range of 0.06.
Table 6
Main regression—the impact of lagged FDI on economic growth
lfdi | 0.0651*** | 0.0656*** | 0.0691*** | 0.0691*** | 0.0635*** | 0.0693*** | 0.0639*** |
| (0.0164) | (0.0165) | (0.00835) | (0.00835) | (0.0165) | (0.00834) | (0.0165) |
L.lfdi | 0.0690*** | 0.0690*** | 0.0629*** | 0.0629*** | 0.0684*** | 0.0638*** | 0.0680*** |
| (0.0162) | (0.0163) | (0.00824) | (0.00824) | (0.0164) | (0.00821) | (0.0162) |
lpop | − 2.919* | − 3.300* | − 0.896 | − 0.896 | − 1.677 | − 0.729 | − 2.566 |
| (1.704) | (1.810) | (0.885) | (0.885) | (2.649) | (0.857) | (1.867) |
ys | − 0.115 | − 0.0702 | 0.0111 | 0.0111 | − 0.0963 | − 0.00517 | − 0.121 |
| (0.118) | (0.143) | (0.0701) | (0.0701) | (0.135) | (0.0577) | (0.120) |
ldi | − 0.296 | − 0.450 | − 0.0727 | − 0.0727 | − 1.110 | 0.0510 | − 0.476 |
| (0.577) | (0.637) | (0.312) | (0.312) | (1.145) | (0.307) | (0.711) |
lgov | 0.641 | 0.696 | 0.507 | 0.507 | − 0.281 | 0.398 | 0.374 |
| (0.802) | (0.835) | (0.408) | (0.408) | (1.215) | (0.392) | (0.829) |
lcre | 0.425 | 0.759 | 0.226 | 0.226 | 0.536 | 0.0627 | 0.317 |
| (0.477) | (0.665) | (0.325) | (0.325) | (0.783) | (0.279) | (0.625) |
inf | − 0.00748 | 0.0122 | 0.00307 | 0.00307 | − 0.00951 | − 0.00357 | − 0.00762 |
| (0.0164) | (0.0324) | (0.0158) | (0.0158) | (0.0189) | (0.00865) | (0.0179) |
Province FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Year FE | No | Yes | Yes | Yes | No | No | No |
Sector FE | No | No | Yes | Yes | No | No | No |
Island FE | No | No | No | Yes | No | No | No |
Year × Province FE | No | No | No | No | Yes | No | No |
Year × Sector FE | No | No | No | No | No | Yes | No |
Year × Island FE | No | No | No | No | No | No | Yes |
Observations | 1943 | 1943 | 1943 | 1943 | 1943 | 1943 | 1943 |
R-squared | 0.411 | 0.411 | 0.860 | 0.860 | 0.418 | 0.861 | 0.412 |
Table
7 reports the results of the estimated impact of FDI by sector on growth. To obtain the results, we interact between FDI and dummy sectoral. Further, we estimate separately for Agricultural FDI since the interactions only provide results for nine sectors (to avoid perfect collinearity among dummy variables). Hence, the coefficient estimated of Agricultural FDI we added to the table.
Table 7
Main regression—the impact of sectoral FDI on economic growth
lfdi | 0.00929 | 0.00962 | 0.00962 |
| (0.0182) | (0.0183) | (0.0183) |
Agricultural FDI | − 0.081** | − 0.081** | − 0.081** |
| (0.0377) | (0.0377) | (0.0377) |
Mining FDI | 0.188*** | 0.189*** | 0.189*** |
| (0.0251) | (0.0252) | (0.0252) |
Manufacturing FDI | 0.196*** | 0.198*** | 0.198*** |
| (0.0237) | (0.0238) | (0.0238) |
Water, Gas, Electricity FDI | 0.0494** | 0.0483** | 0.0483** |
| (0.0232) | (0.0232) | (0.0232) |
Hotel and Restaurant FDI | 0.172*** | 0.174*** | 0.174*** |
| (0.0255) | (0.0255) | (0.0255) |
Trade FDI | 0.0328 | 0.0319 | 0.0319 |
| (0.0255) | (0.0255) | (0.0255) |
Construction FDI | 0.0149 | 0.0154 | 0.0154 |
| (0.0302) | (0.0302) | (0.0302) |
Transportation and Communication FDI | 0.0294 | 0.0273 | 0.0273 |
| (0.0255) | (0.0256) | (0.0256) |
Real Estate FDI | 0.0644** | 0.0636** | 0.0636** |
| (0.0266) | (0.0267) | (0.0267) |
Other Services FDI | 0.0106 | 0.00841 | 0.00841 |
| (0.0264) | (0.0264) | (0.0264) |
Province FE | Yes | Yes | Yes |
Year FE | No | Yes | Yes |
Sector FE | No | No | No |
Island FE | No | No | Yes |
Year × Province FE | No | No | No |
Year × Sector FE | No | No | No |
Year × Island FE | No | No | No |
Control Variables | Yes | Yes | Yes |
Observations | 2380 | 2380 | 2380 |
R-squared | 0.858 | 0.859 | 0.859 |
Columns 1, 2, and 3 present the estimation results after controlling for province fixed effect (Column 1), province and year fixed effect (Column 2), province and island fixed effect (Column 3), and adding control variables. The results generally show that the sectoral effect of FDI on growth varies, and there is no significant difference either both columns, especially for the level of significance for each variable. Only FDI in the agricultural sector has a negative and statistically significant effect on growth. In contrast, the effect of FDI in the mining; manufacturing; water, gas, and electricity; hotels and restaurants; and real estate show positive results and statistically significantly affected growth. The non-significant effect of sectoral FDI is on trade, construction, transportation and communication, and other services sectors. Interestingly, when we compare each sectoral effect of FDI, we conclude that the magnitude of FDI in the manufacturing sector is the highest among sectors.
The sectoral impact of lagged FDI on economic growth is reported in Table
8. In general, there are no significant differences between the estimation findings and the results in Table
7. However, there are minor discrepancies in the level of importance of some of the FDI's lag sectoral effects. The effect of lagged FDI in agriculture indicates a non-statistically significant impact, but the sign is still negative. The impact of lagged FDI in the mining, manufacturing, water, gas, electricity, hotel and restaurant, and real estate sectors, on the other hand, has constantly remained statistically significant and favorable. Meanwhile, the effect of lagged FDI in other sectors such as trade, construction, transportation and communication, and other services remains steady, albeit statistically negligible.
Table 8
Main regression—the impact of lagged sectoral FDI on economic growth
lfdi | − 0.00393 | − 0.00517 | − 0.00517 |
| (0.0246) | (0.0247) | (0.0247) |
Agricultural FDI | − 0.0779 | − 0.0796 | − 0.0796 |
| (0.0529) | (0.0532) | (0.0532) |
Mining FDI | 0.140*** | 0.141*** | 0.141*** |
| (0.0336) | (0.0337) | (0.0337) |
Manufacturing FDI | 0.142*** | 0.147*** | 0.147*** |
| (0.0369) | (0.0371) | (0.0371) |
Water, Gas, Electricity FDI | 0.0549* | 0.0558* | 0.0558* |
| (0.0300) | (0.0301) | (0.0301) |
Hotel and Restaurant FDI | 0.0970*** | 0.0980*** | 0.0980*** |
| (0.0353) | (0.0354) | (0.0354) |
Trade FDI | 0.0552 | 0.0582 | 0.0582 |
| (0.0356) | (0.0357) | (0.0357) |
Construction FDI | 0.0307 | 0.0341 | 0.0341 |
| (0.0438) | (0.0440) | (0.0440) |
Transportation and Communication FDI | 0.0388 | 0.0393 | 0.0393 |
| (0.0334) | (0.0336) | (0.0336) |
Real Estate FDI | 0.0544 | 0.0581 | 0.0581 |
| (0.0363) | (0.0365) | (0.0365) |
Other Services FDI | 0.0273 | 0.0290 | 0.0290 |
| (0.0392) | (0.0393) | (0.0393) |
L.lfdi | − 0.00401 | − 0.00272 | − 0.00272 |
| (0.0244) | (0.0245) | (0.0245) |
L.Agricultural FDI | − 0.072 | − 0.0711 | − 0.0711 |
| (0.0522) | (0.0525) | (0.0525) |
L.Mining FDI | 0.127*** | 0.127*** | 0.127*** |
| (0.0338) | (0.0339) | (0.0339) |
L.Manufacturing FDI | 0.116*** | 0.113*** | 0.113*** |
| (0.0354) | (0.0355) | (0.0355) |
L.Water, Gas, Electricity FDI | 0.0560* | 0.0537* | 0.0537* |
| (0.0303) | (0.0304) | (0.0304) |
L.Hotel and Restaurant FDI | 0.133*** | 0.134*** | 0.134*** |
| (0.0350) | (0.0351) | (0.0351) |
L.Trade FDI | 0.0304 | 0.0274 | 0.0274 |
| (0.0342) | (0.0343) | (0.0343) |
L.Construction FDI | 0.0194 | 0.0178 | 0.0178 |
| (0.0419) | (0.0421) | (0.0421) |
L.Transportation and Communication FDI | 0.0261 | 0.0241 | 0.0241 |
| (0.0318) | (0.0319) | (0.0319) |
L.Real Estate FDI | 0.0649* | 0.0620* | 0.0620* |
| (0.0355) | (0.0357) | (0.0357) |
L.Other Services FDI | 0.0216 | 0.0192 | 0.0192 |
| (0.0379) | (0.0381) | (0.0381) |
Constant | 17.59* | 33.35** | 33.35** |
| (9.507) | (15.45) | (15.45) |
Province FE | Yes | Yes | Yes |
Year FE | No | Yes | Yes |
Sector FE | No | No | No |
Island FE | No | No | Yes |
Year × Province FE | No | No | No |
Year × Sector FE | No | No | No |
Year × Island FE | No | No | No |
Control Variables | Yes | Yes | Yes |
Observations | 1943 | 1943 | 1943 |
R-squared | 0.874 | 0.874 | 0.874 |
5.2 Robustness check
In this session, we present the findings of robustness tests in examining the impact of FDI on growth using the GMM System estimator. Table
9 shows estimation results employing the GMM System technique. The estimate confirms that FDI has a positive and statistically significant effect on growth, indicating the effect of FDI on growth in the present study is robust. Further, the GMM System estimator results show a lower magnitude than regression with high-dimensional fixed effects. Thus, our robustness test result indicates that the regression with high-dimensional fixed effects outperforms the GMM approach in capturing the effect of FDI on growth employing sectoral level data.
Table 9
Robustness check, GMM system—the impact of FDI on economic growth
L.lgdp | 0.884*** |
| (0.045) |
lfdi | 0.009** |
| (0.004) |
lpop | 0.018 |
| (0.025) |
ys | − 0.005 |
| (0.009) |
ldi | 0.057* |
| (0.033) |
lgov | − 0.032 |
| (0.026) |
lcre | 0.052* |
| (0.030) |
inf | − 0.001 |
| (0.002) |
Constant | 0.577 |
| (0.483) |
Observations | 2203 |
Number of sector-province | 314 |
AR(1) | 0.002 |
AR(2) | 0.833 |
Hansen | 0.579 |
Sargan | 0.002 |
Number of Instruments | 18.000 |
We also examine robustness for FDI sectoral impacts. Table
10 displays the outcomes of this test. Using the GMM System approach, we find that the sectoral effect of FDI on economic growth is no different from the estimates in Tables
7 and
8 (using the fixed effect technique). Table
10 shows that agricultural FDI has a continuously negative and statistically significant effect.
Table 10
Robustness check, GMM system—the impact of sectoral FDI on economic growth
Agricultural FDI | − 0.110* | | | | | | | | | |
| (0.064) | | | | | | | | | |
Mining FDI | | 0.027* | | | | | | | | |
| | (0.015) | | | | | | | | |
Manufacturing FDI | | | 0.067*** | | | | | | | |
| | | (0.024) | | | | | | | |
Water, Gas, Electricity FDI | | | | 0.055 | | | | | | |
| | | | (0.061) | | | | | | |
Hotel and Restaurant FDI | | | | | 0.041* | | | | | |
| | | | | (0.024) | | | | | |
Trade FDI | | | | | | 0.012 | | | | |
| | | | | | (0.082) | | | | |
Construction FDI | | | | | | | 0.015 | | | |
| | | | | | | (0.030) | | | |
Transportation and Communication FDI | | | | | | | | 0.014 | | |
| | | | | | | | (0.037) | | |
Real Estate FDI | | | | | | | | | 0.062** | |
| | | | | | | | | (0.030) | |
Other Services FDI | | | | | | | | | | 0.042 |
| | | | | | | | | | (0.069) |
Constant | 0.328 | 2.372 | 0.457 | 1.145 | 0.504 | 0.313 | 0.360* | 0.224 | 0.334 | 1.047 |
| (1.165) | (1.690) | (0.344) | (0.964) | (0.534) | (0.727) | (0.201) | (0.195) | (0.302) | (0.719) |
Control Variables | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Observations | 2,203 | 2,203 | 2,203 | 2,203 | 2,203 | 2,203 | 2,203 | 2,203 | 2,203 | 2,203 |
Number of sector-province | 314 | 314 | 314 | 314 | 314 | 314 | 314 | 314 | 314 | 314 |
AR(1) | 0.016 | 0.006 | 0.011 | 0.074 | 0.002 | 0.007 | 0.004 | 0.006 | 0.001 | 0.004 |
AR(2) | 0.796 | 0.995 | 0.655 | 0.459 | 0.329 | 0.557 | 0.620 | 0.433 | 0.226 | 0.399 |
Hansen | 0.809 | 0.206 | 0.424 | 0.816 | 0.241 | 0.292 | 0.094 | 0.194 | 0.114 | 0.419 |
Sargan | 0.000 | 0.000 | 0.000 | 0.003 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Number of Instruments | 31.000 | 38.000 | 29.000 | 23.000 | 46.000 | 25.000 | 34.000 | 27.000 | 38.000 | 34.000 |
Furthermore, mining, manufacturing, hotels and restaurants, and real estate FDI all have favorable and statistically significant effects. Meanwhile, the effect of FDI in the water, gas, and electricity sectors is not statistically significant, which is fairly surprising. The impact of FDI in this industry differs significantly from the findings in Tables
7 and
8, but the sign remains consistent, namely positive. FDI has a continuously insignificant and favorable impact on other sectors such as trade, construction, transportation and communication, and other services.
5.3 Discussion
This study sets out to assess the importance of FDI in driving growth at the sectoral level. Overall, the current study found that FDI has an enhancing effect on growth in the Indonesian province. This study produced results that corroborate the findings of a great deal of the recent previous empirical work in this field, such as Luu et al. (
2017) and Van Bon (
2019), which found a positive impact of FDI on provincial growth in Vietnam. This finding underscores the importance of FDI in improving growth in Indonesian provinces, which can occur through direct and indirect effects, with the direct effect being an increase in capital stock and the indirect effect being an increase in knowledge stock (Mehic et al.
2013).
Turning to the results of the sectoral effect of FDI, we find that the effect of sectoral FDI on growth differs significantly across sectors. These findings corroborate prior research, which found various sectoral FDI effects (Chakraborty and Nunnenkamp
2008; Vu and Noy
2009; Abouelfarag and Abed
2020). Further, we find that FDI in the agricultural sector has a detrimental influence on growth. Our finding is as expected and consistent with previous findings in which the effect of FDI in the agricultural sector is negative or insignificant (Alfaro
2003; Vu and Noy
2009; Bunte et al.
2018; and Abouelfarag and Abed
2020). This result implies that FDI in the agricultural sector has a weak relationship with the domestic economy and exports oriented (Aykut and Sayek
2007). That reason is plausible for the Indonesian case since foreign enterprises operating in agriculture mainly invest in oil palm. They may export the product in raw material (such as crude palm oil) without first adding value. Another possible explanation for this is that the less technology transfer from FDI in agriculture can be associated with the low absorptive capacity of domestic firms (Aoki and Todo
2008).
The negative impact of agricultural FDI can be attributed to a lack of absorptive capacity of the host country, a subject that has garnered a significant deal of attention in the literature. In Indonesia, there are at least two issues that can lead FDI to have a negative influence on economic growth if it is accompanied by low technology absorption ability (Pasaribu et al.
2021).
First, according to a survey conducted by Statistics Indonesia (BPS) in August 2020, just 9.7% of the workforce in Indonesia is a university graduate. According to Nambiar et al. (
2019), agriculture employs barely 2% of college graduates. As a result, limited labor capacity to absorb FDI-brought technologies in the agriculture sector appears to be a severe issue in driving economic growth.
Second, in the agriculture sector, state-owned enterprises (SOEs) dominate economic activity more than the private sector, including FDI. The government bestows different benefits to SOEs, including incentives, subsidies, and tax cuts. This results in SOEs' dominance in the agricultural sector, which ultimately leads to a lack of competition and market failures such as a lack of supply and high agricultural product prices, potentially undermining economic growth.
In contrast, our findings show that FDI in the mining sector positively impacts growth. This conclusion differs from what is expected in the literature, which generally contends that FDI in extractive industries like mining is detrimental to growth. Our results support Gochero and Boopen's (
2020) empirical finding, which found that FDI in the mining sector has a beneficial influence on Zimbabwean growth. One possible explanation for this finding is an economic turnover impact caused by FDI in the mining sector via a better supply of public goods (Bunte et al.
2018). This indicates that foreign investment in the mining sector contributes to growth by boosting the supply of public goods, hence increasing the economy's efficiency and growth.
Another important finding was that FDI in the manufacturing sector positively affects growth and the magnitude is more potent than FDI in other sectors. The present findings seem consistent with other research that found the positive impact of manufacturing FDI on growth (Chakraborty and Nunnenkamp
2008; Vu and Noy
2009; and Doytch and Uctum
2011). The positive impact of manufacturing FDI suggests that foreign firms have a close link with domestic firms through providing intermediate input (Aykut and Sayek
2007). For instance, in the case of the automotive industry in Jabodetabek, Indonesia (Syah
2019), hence the transfer technology exists. Our result also corroborates the recent empirical evidence of Haini and Tan (
2022), which found that the magnitude of FDI in manufacturing is more prominent than in other sectors. The authors suggest that FDI in the sector would generate enormous growth spillover since the sector has a tremendous potential link with other sectors and intra-industry. Our evidence of the more considerable growth-promoting effect of manufacturing FDI also indicates that attracting FDI in the sector will be the policy option to enhance growth faster.
The evidence on the effect of FDI in the service sector is equivocal, with FDI in the water, gas, electricity, hotel and restaurant, and real estate sectors positively impacting growth. Meanwhile, foreign direct investment (FDI) in trade, construction, transportation and communication, and other service sectors does not affect growth. Because service sector FDI has a distinct character across the industry, these findings should be interpreted cautiously. One probable explanation for this is that FDI in the service sector with a forward link will boost growth (Aykut and Sayek
2007). This argument could explain the influence of FDI on the water, gas, and electricity sectors, the hotel and restaurant industries, and real estate, where the motive is commonly to serve the local market.
Meanwhile, to explain the effect of services FDI in other sectors, the possibility is that service FDI in that sector is based on "soft" knowledge (technical, management and marketing know-how, expertise, organizational skills, and information), which makes it more difficult to transfer knowledge and technology as our findings in the transportation and communication sector (Doytch and Uctum
2011). Furthermore, FDI in that sector is linked to foreign aid's role in accelerating growth. Younsi et al. (
2021) found that aid and FDI have a significant positive complementarity effect on economic growth. Hence, our findings suggest that FDI in the transportation and communication sectors may have less influence on growth when it is not accompanied by aid. Another possible explanation for the insignificant effect is that the effect is likely to be long term rather than short term. The argument is that investing in the transportation and communication sectors may take a long time to reap economic benefits.
The insignificant impact of FDI in construction is in line with the finding of (Haini and Tan
2022). They argued that the FDI services sector, such as construction, has no significant effect on growth, possibly due to tight restrictions related to national security and the difficulty of transmitting knowledge and technology. Another possible reason could be that complicated procedures may hinder the construction of FDI's effect on growth (Abouelfarag and Abed
2020).
Another unresolved issue in responding to the challenge of harnessing FDI to stimulate economic growth is the host country's absorption capacity. Scholars have paid close attention to this issue, and they have formalized a study of the role of absorption capacity in capturing the benefits spread by FDI (see Silajdzic and Mehic
2016; Hanafy and Marktanner
2019). Absorption capacity refers to the ability of domestic firms to absorb FDI-brought technology or knowledge (Görg and Greenaway
2004).
However, the proxies used to capture absorption capacity vary across the literature, such as human capital and domestic firms. According to Tang and Zhang (
2016), absorption capacities such as human capital, government policies that encourage FDI, infrastructure capacity, and research and development are required for China to benefit from FDI in manufacturing exports. Their findings may also imply that intense export activity will stimulate economic growth.
To summarize, the influence of each sectoral FDI ranging from mining, manufacturing, water, gas, electricity, hotels and restaurants, and real estate should be various processes. The processes that can be utilized to explain the effects of FDI in these sectors are through two channels: technology transfer and capital accumulation.
Technology transfer can be used to link FDI in the manufacturing and hotel and restaurant sectors. Zhang's (
2023) findings, for example, reveal that FDI in China establishes R&D centers and focuses on high-tech businesses and knowledge-intensive services. Furthermore, the introduction of new managerial skills in the tourism industry sector might explain how the FDI mechanism in the hotel and restaurant sector can have a favorable effect on economic growth (Sokhanvar
2019); in this context, technology transfer happens.
In the second channel, FDI in the mining, water, gas, electricity, and real estate industries can be linked to capital accumulation, thereby positively impacting economic growth. Vu and Noy (
2009) discovered that FDI in the real estate industry can have a crowding-in effect on domestic capital. Similarly, FDI in the mining and water, gas, and power industries, which are often capital intensive, will stimulate an increase in domestic capital to boost economic growth.